This work is part of the Udacity Nanodegree of the same name. It includes trading Algorithms that use ML, Neural Networks and Deep Learning. Python has been used as the main language and PyTorch as the primary Deep Learning library.

The project work/submissions have been added in the respective sections. Each folder contains a Jupyter Notebook that can be opened statically (file of type project_<number>_starter.ipynb).

The nanogree was split in two parts, covering the following topics:

Learn the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization.

• Project: Trading with Momentum
• Project: Breakout Strategy
• Project: Smart Beta and Portfolio Optimization
• Project: Multi-factor Model

Learn how to analyze alternative data and use machine learning to generate trading signals. Run a backtest to evaluate and combine top performing signals.

• Project: NLP on Financial Statements
• Project: Sentiment Analysis with Neural Networks
• Project: Combining Signals for Enhanced Alpha
• Project: Backtesting

The certificate is under the Certificate folder.